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Quantocracy’s Daily Wrap for 02/11/2024

This is a summary of links featured on Quantocracy on Sunday, 02/11/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Simple, Effective Way to Manage Turnover and Not Get Killed by Costs [Robot Wealth]

    Every time we trade, we incur a cost. We pay a commission to the exchange or broker, we cross spreads, and we might even have market impact to contend with. A common issue in quant trading is to find an edge, only to discover that if you executed it naively, youd get killed with costs. In this article, Ill show you an example of using a simple heuristic that helps you do an optimal amount of
  • How to exploit the month-end flow effect for a 502% return [PyQuant News]

    Fund managers report their holdings every month. They dont want to tell investors that they lost money the latest meme stock. So they will sell the meme stocks and buy higher quality assets, like bonds. We might be able to take advantage of this month-end flow effect by buying bonds toward the end of the month and selling them at the beginning. The month-end flow effect is one of many
  • Generic derivative returns and carry (for strategy testing) [SR SV]

    Backtesting of macro trading strategies requires good approximate profit-and-loss data for standard derivatives positions, particularly in equity, foreign exchange, and rates markets. Practical calculation methods of generic proxy returns not only deliver valid strategy targets but are also the basis of volatility adjustments of trading factors and for calculating nominal and real carry of
  • Band of Brothers Attacking Short Sellers: Game Stop for Hedge Funds [Alpha Architect]

    In our book The Incredible Shrinking Alpha, Andrew Berkin and I presented the evidence demonstrating that the markets have become more efficient over time, making it more difficult to outperform the market on a risk-adjusted basis. Market efficiency explains the lack of persistent outperformance of actively managed funds beyond the randomly expected. Among the reasons we cited for the shrinking
  • Research Review | 9 February 2024 | Cross Market Analytics [Capital Spectator]

    A Changing Stock-Bond Correlation: Explaining Short-term Fluctuations Garth Flannery (BlueCove) and Daniel Bergstresser (Brandeis Intl Business School) December 2023 This paper builds on a framework that uses macroeconomic drivers to explain long-term variation in the correlation between stocks and bonds. The existing work focuses on the relative volatility of growth and inflation and the

Quantocracy’s Daily Wrap for 02/07/2024

This is a summary of links featured on Quantocracy on Wednesday, 02/07/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 02/05/2024

This is a summary of links featured on Quantocracy on Monday, 02/05/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introducing max-GM, a new(?) performance statistic [Investment Idiocy]

    Do you remember this post? https://qoppac.blogspot.com/2022/06/vol-targeting-cagr-race.html Here I introduced a performance metric, the best annualised compounding return at the optimal leverage level for that strategy. This is equivalent to finding the highest geometric return once a strategy is run at it's Kelly optimal leverage. I've since played with that idea a bit, for example in
  • HY Bonds = High or Hazardous Yield? [Finominal]

    The correlation of high yield (HY) to investment-grade (IG) bonds has been increasing HY bonds can simply be replicated via a combination of the S&P 500 and IG bonds Replication portfolios offer better Sharpe ratios, which makes a case against using HY bonds in asset allocation INTRODUCTION When we recently ran a peer review analysis for BlackRocks 60/40 Target Allocation Fund (BIGPX) using

Quantocracy’s Daily Wrap for 02/04/2024

This is a summary of links featured on Quantocracy on Sunday, 02/04/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Replacing the 40, in R [Babbage9010]

    Elliot Rozner published a blog post recently proposing that one could replace the 40% bond portion of a 60/40 portfolio with a Long/Short equity trend following strategy. Here well put that idea (and his suggested approach) into R using quantmod and examine the components, and finally suggest a free-lunch tweak to improve the risk-adjusted returns further. Perhaps Im being a little flip
  • Overcoming experimenter bias in scientific research and finance [Mathematical Investor]

    Reproducibility has emerged as a major issue in numerous fields of scientific research, ranging from psychology, sociology, economics and finance to biomedicine, scientific computing and physics. Many of these difficulties arise from experimenter bias (also known as selection bias): consciously or unconsciously excluding, ignoring or adjusting certain data that do not seem to be in agreement

Quantocracy’s Daily Wrap for 02/03/2024

This is a summary of links featured on Quantocracy on Saturday, 02/03/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to do interest rate analysis with multi-factor models [PyQuant News]

    Interest rates are the driving force behind the economy. They influence everything from the cost of purchasing a home to a companys decision on capital investments. Quants model how interest rate changes impact portfolios using principal component analysis (PCA). (They also do it for stock portfolios!) In todays newsletter, well look at how interest rate movements can be broken down.

Quantocracy’s Daily Wrap for 02/02/2024

This is a summary of links featured on Quantocracy on Friday, 02/02/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantifying and Combining Crypto Alphas [Robot Wealth]

    In this article, Ill take some crypto stat arb features from our recent brainstorming article and show you how you might quantify their strength and decay characteristics and then combine them into a trading signal. This article continues our recent articles on stat arb: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas Ideas for crypto stat
  • How to Build a Systematic Innovation Factor in Stocks [Quantpedia]

    The aim of this article is multifold. It aims to answer the research question: does a portfolio consisting of top innovators outperform the S&P 500 index? To address this question, a strategy of investing long in top innovators according to their ranking is developed, and its performance is compared to that of the broad-based index. Based on the common belief that higher innovativeness carries
  • Trend to Passive Investing Negatively Affecting Active Funds [Alpha Architect]

    In our book, The Incredible Shrinking Alpha, Andrew Berkin and I identified four key trends that were increasing the hurdles for active managers in their quest to generate alpha: Academic research has been converting what was once alpha into beta (common factors that could be accessed at much lower costs through systematic funds such as index funds). The pool of victims (naive retail

Quantocracy’s Daily Wrap for 01/29/2024

This is a summary of links featured on Quantocracy on Monday, 01/29/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Replacing the 40 [Return Sources]

    The 60/40 portfolio (60 percent stocks, 40 percent bonds) has become such a classic, that for many investors, the word portfolio means 60/40 by default. Looking at the past few decades, its easy to see why this is the case. Stocks, (or at least U.S. stocks), have had outstanding performance. Since stocks are much more volatile than bonds, the vast majority of the returns to 60/40 come
  • Institutional portfolio managers – better at buying or selling? [Alpha Architect]

    What are the Research Questions? This paper examines the decisions of sophisticated market participants experienced institutional portfolio managers (PMs) and the authors ask the following questions: Is there a significant difference in performance between buying and selling decisions made by institutional PMs? Is there an asymmetric allocation of limited cognitive resources towards buying
  • Monte Carlo Simulations: Forecasting Folly? [Finominal]

    Financial advisors primarily use Monte Carlo simulations to forecast returns However, this methodology is flawed as it ignores the valuations of asset classes Using capital market assumptions is likely a better approach INTRODUCTION The Shanghai Composite Index (SSE) was booming in early 2015, and as it soared, legions of new investors rushed in to try their luck at securities speculation.

Quantocracy’s Daily Wrap for 01/28/2024

This is a summary of links featured on Quantocracy on Sunday, 01/28/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Join the Race: Quantpedia Awards 2024 Await You [Quantpedia]

    Hello everyone, Two weeks ago, we promised you a surprise, and now its finally time to unveil what we have prepared for you :). Our Quantpedia Awards 2024 aims to be the premier competition for all quantitative trading researchers. If you have an idea in your head about systematic/quantitative trading or investment strategy, and you would like to gain visibility on the professional scene, then
  • Ideas for Crypto Stat Arb Features [Robot Wealth]

    This article continues our recent articles on stat arb: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas In this article, Ill brainstorm some ideas for predictive features that you could potentially use in a crypto stat arb model. The ideas draw insights from recent discussions and market observations, but of course, you should do your own
  • Equity market timing: the value of consumption data [SR SV]

    The dividend discount model suggests that stock prices are negatively related to expected real interest rates and positively to earnings growth. The economic position of households or consumers influences both. Consumer strength spurs demand and exerts price pressure, thus pushing up real policy rate expectations. Meanwhile, tight labor markets and high wage growth shift national income from
  • Moving Average Distance and Time-Series Momentum [Alpha Architect]

    Because of the strong evidence, momentum continues to receive much attention from researchers. Out of the hundreds of exhibits in the factor zoo, one of just five equity factors that met all the criteria (persistent, pervasive, robust, implementable, and intuitive) Andrew Berkin and I established in our book Your Complete Guide to Factor-Based Investing was momentum (both cross-sectional
  • Quickly compute Value at Risk with Monte Carlo [PyQuant News]

    Value at risk (VaR) is a tool professional traders use to manage risk. It estimates how much a portfolio might lose, given normal market conditions, over a set time period. There are three ways to compute VaR: the parametric method, the historical method, and the Monte Carlo method. In contrast to the parametric and historical methods which are backward looking, Monte Carlo is forward looking. In

Quantocracy’s Daily Wrap for 01/24/2024

This is a summary of links featured on Quantocracy on Wednesday, 01/24/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean Reversion vs Trend Following Through the Years [Alvarez Quant Trading]

    Something I am always thinking about is how the markets are behaving now vs the past few years vs several years ago. My edge on the strategies I trade depends on two main ideas. One, current market behavior is similar to what I tested on which is normally the last 5-10 years. Two, not too many others have found the same edge. Unfortunately for (2), more and more people are trading quant style and

Quantocracy’s Daily Wrap for 01/22/2024

This is a summary of links featured on Quantocracy on Monday, 01/22/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A General Approach for Exploiting Statistical Arbitrage Alphas [Robot Wealth]

    Last week, I wrote a short article about statistical arbitrage trading in the real world. Statistical arbitrage is a well-understood concept: find pairs or baskets of assets you expect to move together, wait for them to diverge, and bet on them converging again. Simple enough. But making it work, especially at scale, is a little more complicated. A somewhat old-school approach takes pairs of
  • Easily compare investment strategies [PyQuant News]

    Portfolio optimization is a balance between maximizing returns and minimizing risk. While it might sound easy, its actually very difficult compare investment strategies. First, we have to accurately forecast future returns and risk. Then, we have to use tricky optimization models to build the portfolios subject to our constraints. Not to mention come up with a strategy that works! Most
  • Outperforming Cap- (Value-) Weighted and Equal-Weighted Portfolios [Alpha Architect]

    Popular benchmarks in academic research studies to evaluate the performance of investment strategies are cap-weighted (market-, or value-weighted), and equal-weighted portfolios. Capitalization-weighted portfolios are used because they are the simplest and cheapest to implement, representing the total market with little to no rebalancing costs. Equal-weighted portfolios have produced higher
  • Trend Following in Bear Markets [Finominal]

    Short-only trend following in stocks generated consistent losses across markets However, combining the strategy with an equities portfolio generated diversification benefits Like other hedging strategies it would be difficult to execute this strategy over the long-term INTRODUCTION Trend following is likely the most researched investment strategy. The folks at AQR have backtested the framework